Kerja Sepenuh Masa, AI Robotics Engineer di Multitude Waves - Maukerja

We are partnering with an Applied R&D Lab building advanced robotic systems and software stacks.

Our client bridges the gap between simulation and real-world physical systems. They move fast, deploy code directly to physical hardware, and open-source their technology. If your background is in training and deploying policies that control physical systems rather than just sitting in simulations or research papers, this role offers rapid iteration cycles and high visibility.

• Deploy on Hardware: Design, train, and deploy Reinforcement Learning (RL) and imitation learning policies for locomotion, manipulation, or whole-body control directly on physical robots.
• Bridge Sim-to-Real: Conduct hardware experiments and close the sim-to-real gap through domain adaptation, noise injection, and systematic debugging.
• Build Infrastructure: Develop high-throughput simulation environments (MuJoCo, IsaacLab) and data pipelines that support rapid policy iteration.
• Iterate & Debug: Instrument robotic deployments, diagnose physical failure modes, and feed insights directly back into policy training.
• Cross-Functional Collaboration: Partner closely with hardware, firmware, and infrastructure teams to address physical constraints and ensure policy robustness.

• Core Machine Learning: Strong foundations in Reinforcement Learning (RL) or Imitation Learning, with proven experience training policies that run on real systems.
• Hardware-First Mindset: Comfortable working directly with physical robots—not just simulators. You prioritize empirical, working results over theoretical models.
• Technical Stack: Proficient in Python and standard ML/RL frameworks (PyTorch, JAX, IsaacGym/IsaacLab, MuJoCo, etc.).
• Agility: Fast-paced problem solver capable of switching seamlessly between research-level algorithms and hands-on engineering debugging.

• Prior work on legged or advanced robotic platforms.
• Demonstrated experience with sim-to-real transfer techniques (domain randomization, system identification).
• Active contributions to open-source robotics projects.
• Background in control theory, trajectory optimization, or robot dynamics.

• Fast-Track Interview Process: Direct access to the hiring team and hiring manager feedback.
• High-Impact Work: Work on production-grade physical systems with short feedback loops—your code moves real hardware within days, not quarters.
• Transparent Growth: Competitive compensation package, high team visibility, and the opportunity to contribute significantly to the open-source robotics community.

Interested candidates are invited to submit their resume detailing relevant hands-on robotics or RL project experience.